The platform uses geospatial data and satellite imagery to provide data-based applications for mineral exploration and discovery and promises to increase hypothesis testing and the speed of the exploration lifecycle. "Traditionally, remote sensing is carried out by specialists (remote sensing geologists) on behalf of the mineral exploration team. Although they still have a role in supporting the process, the Descartes Labs platform puts the technology into the hands of the exploration geologists who know the project areas the best. By leveraging the data obtained from satellite and airborne imagery, they can accelerate their hypothesis formulation and exploration strategies to find new deposits," James Orsulak, senior director of business and sales at Descartes Labs, told MINING.COM. MDC: Your platform puts emphasis on the data refinery.

Satellite imagery holds a wealth of information that could be useful for industries, science and humanitarian causes, but one big and persistent challenge with it has been a lack of effective ways to tap that disparate data for specific ends. That's created a demand for better analytics, and now, one of the startups that has been building solutions to do just that is announcing a round of funding as it gears up for expansion. Descartes Labs, a geospatial imagery analytics startup out of Santa Fe, New Mexico, is today announcing that it has closed a $20 million round of funding, money that CEO and founder Mark Johnson described to me as a bridge round ahead of the startup closing and announcing a larger growth round. The funding is being led by Union Grove Venture Partners, with Ajax Strategies, Crosslink Capital, and March Capital Partners (which led its previous round) also participating. It brings the total raised by Descartes Labs to $60 million, and while Johnson said the startup would not be disclosing its valuation, PitchBook notes that it is $220 million ($200 million pre-money in this round).

Too often in the past, data science has been a siloed activity that centered around data exploration and insight without truly crossing departmental lines. Those limitations have kept data science from becoming a strategic, enterprise-wide initiative for supporting multiple data science and machine learning projects. Technologies have now matured and become cost-effective enough that an enterprise-class, data science platform can support large scale production requirements. In response, enterprises are directing data science activities across multiple lines of business, using a set of standardized approaches that embrace openness, extensibility, and adaptability. We're now offering Data Science Experience version 1.2, which continues to address all three recommendations above.